Thursday, September 13, 2018

NASA and Development Seed are tracking Hurricane Florence using machine learning techniques, producing results six times faster than current capabilities.
Their neural network-based approach calculates hurricane strength and wind speed by monitoring live imagery as it’s delivered from weather satellites.
This allows NASA to create estimates hourly, a significant speedup from the usual six-hour cycle.

The primary factor for estimating a hurricane’s destructive potential is wind speed.
By creating faster, more reliable estimates of storm wind speeds, authorities may be able to make better decisions about moving people out of harm’s way and moving resources where they’re needed.
These decisions can help save both life and property.
The issue is growing in urgency: the 2017 hurricane season was the most destructive on record, claiming thousands of lives and causing an estimated $280 billion in damage.

Estimates of cyclone intensity rely upon the Dvorak technique, which matches satellite imagery of a storm to known patterns.
Once matched, it’s possible to estimate wind speed.
AI experts at NASA’s Marshall Spaceflight Center and Development Seed trained neural networks using historical hurricane imagery and classifications, allowing this workflow to be fully automated.

The view of the Atlantic on Sept. 12. Florence on the right, bearing close to the US coast, Tropical Storm Isaac near the Lesser Antilles, and Hurricane Helene off the coast of Africa.image : NOAA